It is widely recognized that object detection is a challenging problem. There are many aspects that make object detection difficult for computer vision systems, including factors such as variations in image acquisition, complexity of object appearance, and significant variability in object backgrounds (usually referred to as clutter), to name just a few. In the domain of medical imaging, an “object” might refer to a particular component of normal anatomy, the location of a non-anatomical object, or the presence of disease such as a tumor.
One important application of object detection in medical imaging is the detection of lung nodules, or masses, in CT scans of the chest. Despite more than two decades of effort, the general problem of machine nodule detection remains unsolved, and human detection remains limited. We argue that a significant reason for this is a failure to address one significant component of what makes the problem difficult: the complex interaction of nodules with pulmonary vessels, and the variation in appearance due to varying acquisition protocols.